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Record W2412687220 · doi:10.21427/d7g03p

A Comparative Study of Performance Management Practice in the Republic of Ireland, as Influenced by it's Expansion from the Private Sector to its Public Equivalent.

2016· article· en· W2412687220 on OpenAlexfundno aff
Kevin Paul Corbett

Bibliographic record

VenueArrow - TU Dublin (Technological University Dublin) · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Analysis and Policy
Canadian institutionsnot available
FundersHealth Service ExecutiveUniversity of GalwayCanadian Institute of Steel ConstructionTechnological University DublinNational University of Ireland
KeywordsPublic sectorPrivate sectorBusinessPublic administrationPublic relationsPolitical scienceEconomic growthEconomicsLaw

Abstract

fetched live from OpenAlex

This is a study of performance management (PM) that examines its comparative practice in the Republic of Ireland, as influenced by its expansion from the private sector to the public equivalent. The research objectives of this study are to establish the level of incidence of PM practice in both sectors, to consider how PM is practised, including the mechanisms employed, to determine the objectives of the PM processes for these organisations and to ascertain the comparative perceived impact and effectiveness of PM. It was established that there was a gap of nine years since the last quantitative analysis of the subject matter in Ireland had been conducted. A random sample based on five strata was deployed for the purpose of the primary research. A self-administered questionnaire was distributed nationwide in 2007/08 to 499 organisations, yielding a 41% (n=204) useable response rate. The findings confirm a high level of PM practice in Ireland, particularly in the public sector. Probably the most significant discovery, however, is that the vast majority of respondents believe PM to be effective. Nevertheless, its level of effectiveness is deemed significantly higher in private sector. The top three objectives of performance management systems (PMS) across both sectors are to agree key objectives, improve future performance and provide feedback on current or past performance. The survey evidence also reveals the main inhibitors of PM to be the perceived lack of follow up and support by management to agreed PM outcomes, failure to review or monitor the system and the presence of too much paperwork. Both sectors are in accord regarding the key goal of their system, which is to agreeing key objectives with staff. Hence, it was also established that ‘objective setting’ was the most popular and effective mechanism or scheme type of appraisal used. Of the features of PM, performance-related pay (PRP) is growing in the private sector, and, it is considered by researcher that this study offers for the first time, empirical evidence of its presence in the public sector. This research has successfully filled the research gap of nine years on PM practice in Ireland in both the private and public sector. It demonstrates to the reader the advances made by both sectors in this regard over the past 50 years. The research has been limited by an absence of analysis of PM vis à vis a number of areas such as the management of change, high performance work systems, employee engagement, the separation of performance review from the pay review and how to manage the underperformer. However, it does reveal the opportunity for further study, e.g. a qualitative analysis of the topic focusing on the opinion of the employee and line manager and their perception of PM.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.057
GPT teacher head0.254
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2016
Admission routes1
Has abstractyes

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